
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 							lowerCamelCase_   (      UpperCAmelCase_				:					Tuple				,      UpperCAmelCase_				:					List[str]				,      UpperCAmelCase_				:					Optional[Any]							):
						lowercase							:     Any									=	AutoConfig.from_pretrained(lowerCAmelCase__							)
						lowercase							:     Dict									=	FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__							)
						lowercase							:     Optional[Any]									=	checkpoints.load_tax_checkpoint(lowerCAmelCase__							)

						lowercase							:     Any									=	"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

						if config.model_type == "t5":
												lowercase							:     List[str]									=	"""SelfAttention"""
						if config.model_type == "longt5" and config.encoder_attention_type == "local":
												lowercase							:     Optional[Any]									=	"""LocalSelfAttention"""
						elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase							:     Optional[int]									=	"""TransientGlobalSelfAttention"""
						else:
												raise ValueError(
												    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
												    ''' attribute with a value from [\'local\', \'transient-global].'''							)

						# Encoder
						for layer_index in range(config.num_layers							):
												lowercase							:     str									=	f'''layers_{str(lowerCAmelCase__							)}'''

												# Self-Attention
												lowercase							:     Union[str, Any]									=	tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
												lowercase							:     List[str]									=	tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
												lowercase							:     int									=	tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
												lowercase							:     str									=	tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

												# Global input layer norm
												if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																		lowercase							:     int									=	tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

												# Layer Normalization
												lowercase							:     Dict									=	tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

												if split_mlp_wi:
																		lowercase							:     Optional[int]									=	tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
																		lowercase							:     int									=	tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
												else:
																		lowercase							:     List[str]									=	tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

												lowercase							:     str									=	tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

												# Layer Normalization
												lowercase							:     int									=	tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

												# Assigning
												lowercase							:     Union[str, Any]									=	flax_model.params["""encoder"""]["""block"""][str(lowerCAmelCase__							)]["""layer"""]
												lowercase							:     str									=	tax_attention_key
												lowercase							:     Any									=	tax_attention_out
												lowercase							:     List[str]									=	tax_attention_query
												lowercase							:     Any									=	tax_attention_value

												lowercase							:     List[str]									=	tax_attention_layer_norm

												# Global input layer norm
												if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																		lowercase							:     List[Any]									=	tax_global_layer_norm

												if split_mlp_wi:
																		lowercase							:     int									=	tax_mlp_wi_a
																		lowercase							:     Union[str, Any]									=	tax_mlp_wi_a
												else:
																		lowercase							:     Tuple									=	tax_mlp_wi

												lowercase							:     Union[str, Any]									=	tax_mlp_wo
												lowercase							:     Optional[Any]									=	tax_mlp_layer_norm

												lowercase							:     Tuple									=	flax_model_encoder_layer_block

						# Only for layer 0:
						lowercase							:     str									=	tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
						lowercase							:     List[Any]									=	tax_encoder_rel_embedding

						# Side/global relative position_bias + layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												lowercase							:     Union[str, Any]									=	tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
												lowercase							:     Dict									=	tax_encoder_global_rel_embedding

						# Assigning
						lowercase							:     Union[str, Any]									=	tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
						lowercase							:     Union[str, Any]									=	tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_layers							):
												lowercase							:     Union[str, Any]									=	f'''layers_{str(lowerCAmelCase__							)}'''

												# Self-Attention
												lowercase							:     Any									=	tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
												lowercase							:     Optional[Any]									=	tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
												lowercase							:     int									=	tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
												lowercase							:     int									=	tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

												# Layer Normalization
												lowercase							:     Tuple									=	tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
												    """scale"""
												]

												# Encoder-Decoder-Attention
												lowercase							:     Any									=	tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
												lowercase							:     Union[str, Any]									=	tax_enc_dec_attention_module["""key"""]["""kernel"""]
												lowercase							:     Tuple									=	tax_enc_dec_attention_module["""out"""]["""kernel"""]
												lowercase							:     str									=	tax_enc_dec_attention_module["""query"""]["""kernel"""]
												lowercase							:     Any									=	tax_enc_dec_attention_module["""value"""]["""kernel"""]

												# Layer Normalization
												lowercase							:     Optional[Any]									=	tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

												# MLP
												if split_mlp_wi:
																		lowercase							:     List[Any]									=	tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
																		lowercase							:     str									=	tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
												else:
																		lowercase							:     Any									=	tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

												lowercase							:     List[str]									=	tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

												# Layer Normalization
												lowercase							:     Optional[Any]									=	tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

												# Assigning
												lowercase							:     Any									=	flax_model.params["""decoder"""]["""block"""][str(lowerCAmelCase__							)]["""layer"""]
												lowercase							:     Union[str, Any]									=	tax_attention_key
												lowercase							:     List[Any]									=	tax_attention_out
												lowercase							:     Optional[int]									=	tax_attention_query
												lowercase							:     List[Any]									=	tax_attention_value

												lowercase							:     Dict									=	tax_pre_attention_layer_norm

												lowercase							:     List[Any]									=	tax_enc_dec_attention_key
												lowercase							:     Tuple									=	tax_enc_dec_attention_out
												lowercase							:     Optional[Any]									=	tax_enc_dec_attention_query
												lowercase							:     Any									=	tax_enc_dec_attention_value

												lowercase							:     Any									=	tax_cross_layer_norm

												if split_mlp_wi:
																		lowercase							:     List[Any]									=	tax_mlp_wi_a
																		lowercase							:     Optional[int]									=	tax_mlp_wi_a
												else:
																		lowercase							:     Dict									=	tax_mlp_wi

												lowercase							:     Dict									=	tax_mlp_wo

												lowercase							:     int									=	txa_mlp_layer_norm

												lowercase							:     Tuple									=	flax_model_decoder_layer_block

						# Decoder Normalization
						lowercase							:     List[Any]									=	tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
						lowercase							:     Tuple									=	txa_decoder_norm

						# Only for layer 0:
						lowercase							:     str									=	tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
						lowercase							:     Optional[Any]									=	tax_decoder_rel_embedding

						# Token Embeddings
						lowercase							:     Optional[int]									=	tax_model["""target"""]["""token_embedder"""]["""embedding"""]
						lowercase							:     Optional[Any]									=	txa_token_embeddings

						# LM Head (only in v1.1 and LongT5 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												lowercase							:     Dict									=	tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

						flax_model.save_pretrained(lowerCAmelCase__							)
						print('''T5X Model was sucessfully converted!'''							)


if __name__ == "__main__":
				snake_case__					=				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
				)
				parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
				parser.add_argument(
				    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
				)
				snake_case__					=				parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







def  lowerCAmelCase_	(lowerCAmelCase__:		list  ):





			"""simple docstring"""


			if len(lowerCAmelCase__  ) <= 1:
						return [tuple(lowerCAmelCase__  )]

			UpperCAmelCase_:		List[Any]							      =     []

			def generate(lowerCAmelCase__:		int		,						lowerCAmelCase__:		list  ):
						if k == 1:
									res.append(tuple(arr[:]  )  )
									return

						generate(k - 1		,						lowerCAmelCase__  )

						for i in range(k - 1  ):
									if k % 2 == 0:  # k is even
												UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     arr[k - 1], arr[i]
									else:  # k is odd
												UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     arr[k - 1], arr[0]
									generate(k - 1		,						lowerCAmelCase__  )

			generate(len(lowerCAmelCase__  )		,						lowerCAmelCase__  )
			return res


if __name__ == "__main__":
	a      :  Optional[Any]			     =			input('Enter numbers separated by a comma:\n').strip()
	a      :  str			     =			[int(item) for item in user_input.split(',')]
	print(heaps(arr))






